Reference Point Based Multi-Objective Optimization Using Hybrid Artificial Immune System

During the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI).There has been increasing interest in the development of computational models inspired by several immunological principles. Although there are advantages of knowing the range of each objective for Pareto-optimality and the shape of the Pareto-optimal frontier itself in a problem for an adequate decision-making, the task of choosing a single preferred Pareto optimal solution is also an important task. In this paper, a Reference Point Based Multi-Objective Optimization Using hybrid Artificial intelligent approach based on the clonal selection principle of Artificial Immune System (AIS) and Neural Networks is proposed. And, instead of one solution, a preferred set of solutions near the reference points can be found. Modified Multi-objective Immune System Algorithm (MISA) is proposed with real parameters value not binary coded parameters, uniform and non uniform mutation operator is applied to the clones produced. Real parameter MISA works on continuous search space.